[机器学习在急性呼吸窘迫综合征中的应用进展]
Weijun Zhang1, Jianxiao Chen, Yuan Gao
1Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China. Corresponding author: Gao Yuan,
Zhonghua wei zhong bing ji jiu yi xue
|June 27, 2023
概括
机器学习有助于通过分析用于临床决策的复杂数据来理解急性呼吸困扰综合征 (ARDS). 这篇评论探讨了它在ARDS表型,预测和预后中的应用.
科学领域:
- 肺部医学 肺部医学
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
背景情况:
- 急性呼吸困扰综合征 (ARDS) 呈现出低氧化和肺不透明,缺乏特定治疗,死亡率高.
- ARDS的复杂性,快速进展和异质性阻碍了有效的临床管理和治疗策略.
- 当前的数据分析方法与ARDS数据的复杂性作斗争,需要先进的分析方法.
结论:
- 机器学习显示了改善ARDS临床管理的巨大潜力.
- 对可解释的ML模型的进一步研究可以提高临床采用和患者的结果.
- ML为解决ARDS异质性和复杂性带来的挑战提供了一个有希望的途径.
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